Face Class Reference


                    
                    
                    +createFacemarkAAM

Declaration

Objective-C

+ (Facemark*)createFacemarkAAM NS_SWIFT_NAME(createFacemarkAAM());

Swift

class func createFacemarkAAM() -> Facemark


                    
                    
                    +createFacemarkKazemi

Declaration

Objective-C

+ (Facemark*)createFacemarkKazemi NS_SWIFT_NAME(createFacemarkKazemi());

Swift

class func createFacemarkKazemi() -> Facemark


                    
                    
                    +createFacemarkLBF

Declaration

Objective-C

+ (Facemark*)createFacemarkLBF NS_SWIFT_NAME(createFacemarkLBF());

Swift

class func createFacemarkLBF() -> Facemark


                    
                    
                    +getFacesHAAR:faces:face_cascade_name:

Default face detector This function is mainly utilized by the implementation of a Facemark Algorithm. End users are advised to use function Facemark::getFaces which can be manually defined and circumvented to the algorithm by Facemark::setFaceDetector.

Example of usage

std::vectorcv::Rect faces; CParams params(“haarcascade_frontalface_alt.xml”); cv::face::getFaces(frame, faces, &params); for(int j=0;j<faces.size();j++){ cv::rectangle(frame, faces[j], cv::Scalar(255,0,255)); } cv::imshow(“detection”, frame);

Declaration

Objective-C

+ (BOOL)getFacesHAAR:(nonnull Mat *)image
                faces:(nonnull Mat *)faces
    face_cascade_name:(nonnull NSString *)face_cascade_name;

Swift

class func getFacesHAAR(image: Mat, faces: Mat, face_cascade_name: String) -> Bool

Parameters

`image`	The input image to be processed.
`faces`	Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.


                    
                    
                    +loadDatasetList:annotationList:images:annotations:

A utility to load list of paths to training image and annotation file.

Example of usage:

String imageFiles = “images_path.txt”; String ptsFiles = “annotations_path.txt”; std::vector images_train; std::vector landmarks_train; loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);

Declaration

Objective-C

+ (BOOL)loadDatasetList:(nonnull NSString *)imageList
         annotationList:(nonnull NSString *)annotationList
                 images:(nonnull NSArray<NSString *> *)images
            annotations:(nonnull NSArray<NSString *> *)annotations;

Swift

class func loadDatasetList(imageList: String, annotationList: String, images: [String], annotations: [String]) -> Bool

Parameters

`imageList`	The specified file contains paths to the training images.
`annotationList`	The specified file contains paths to the training annotations.
`images`	The loaded paths of training images.
`annotations`	The loaded paths of annotation files.


                    
                    
                    +loadFacePoints:points:offset:

A utility to load facial landmark information from a given file.

Example of usage

std::vector points; face::loadFacePoints(“filename.txt”, points, 0.0f);

The annotation file should follow the default format which is

version: 1 n_points: 68 { 212.716603 499.771793 230.232816 566.290071 … }

where n_points is the number of points considered and each point is represented as its position in x and y.

Declaration

Objective-C

+ (BOOL)loadFacePoints:(nonnull NSString *)filename
                points:(nonnull Mat *)points
                offset:(float)offset;

Swift

class func loadFacePoints(filename: String, points: Mat, offset: Float) -> Bool

Parameters

`filename`	The filename of file contains the facial landmarks data.
`points`	The loaded facial landmark points.
`offset`	An offset value to adjust the loaded points.


                    
                    
                    +loadFacePoints:points:

A utility to load facial landmark information from a given file.

Example of usage

std::vector points; face::loadFacePoints(“filename.txt”, points, 0.0f);

The annotation file should follow the default format which is

version: 1 n_points: 68 { 212.716603 499.771793 230.232816 566.290071 … }

where n_points is the number of points considered and each point is represented as its position in x and y.

Declaration

Objective-C

+ (BOOL)loadFacePoints:(nonnull NSString *)filename
                points:(nonnull Mat *)points;

Swift

class func loadFacePoints(filename: String, points: Mat) -> Bool

Parameters

`filename`	The filename of file contains the facial landmarks data.
`points`	The loaded facial landmark points.


                    
                    
                    +loadTrainingData:images:facePoints:delim:offset:

A utility to load facial landmark dataset from a single file.

/home/user/ibug/image_003_1.jpg 336.820955 240.864510 334.238298 260.922709 335.266918 … /home/user/ibug/image_005_1.jpg 376.158428 230.845712 376.736984 254.924635 383.265403 …

Example of usage

cv::String imageFiles = “../data/images_train.txt”; cv::String ptsFiles = “../data/points_train.txt”; std::vector images; std::vectorstd::vector<Point2f > facePoints; loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSString *)filename
                  images:(nonnull NSArray<NSString *> *)images
              facePoints:(nonnull Mat *)facePoints
                   delim:(char)delim
                  offset:(float)offset;

Swift

class func loadTrainingData(filename: String, images: [String], facePoints: Mat, delim: Int8, offset: Float) -> Bool

Parameters

`filename`	The filename of a file that contains the dataset information. Each line contains the filename of an image followed by pairs of x and y values of facial landmarks points separated by a space. Example
`images`	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
`facePoints`	The loaded landmark points for all training data.
`delim`	Delimiter between each element, the default value is a whitespace.
`offset`	An offset value to adjust the loaded points.


                    
                    
                    +loadTrainingData:images:facePoints:delim:

A utility to load facial landmark dataset from a single file.

/home/user/ibug/image_003_1.jpg 336.820955 240.864510 334.238298 260.922709 335.266918 … /home/user/ibug/image_005_1.jpg 376.158428 230.845712 376.736984 254.924635 383.265403 …

Example of usage

cv::String imageFiles = “../data/images_train.txt”; cv::String ptsFiles = “../data/points_train.txt”; std::vector images; std::vectorstd::vector<Point2f > facePoints; loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSString *)filename
                  images:(nonnull NSArray<NSString *> *)images
              facePoints:(nonnull Mat *)facePoints
                   delim:(char)delim;

Swift

class func loadTrainingData(filename: String, images: [String], facePoints: Mat, delim: Int8) -> Bool

Parameters

`filename`	The filename of a file that contains the dataset information. Each line contains the filename of an image followed by pairs of x and y values of facial landmarks points separated by a space. Example
`images`	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
`facePoints`	The loaded landmark points for all training data.
`delim`	Delimiter between each element, the default value is a whitespace.


                    
                    
                    +loadTrainingData:images:facePoints:

A utility to load facial landmark dataset from a single file.

/home/user/ibug/image_003_1.jpg 336.820955 240.864510 334.238298 260.922709 335.266918 … /home/user/ibug/image_005_1.jpg 376.158428 230.845712 376.736984 254.924635 383.265403 …

Example of usage

cv::String imageFiles = “../data/images_train.txt”; cv::String ptsFiles = “../data/points_train.txt”; std::vector images; std::vectorstd::vector<Point2f > facePoints; loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSString *)filename
                  images:(nonnull NSArray<NSString *> *)images
              facePoints:(nonnull Mat *)facePoints;

Swift

class func loadTrainingData(filename: String, images: [String], facePoints: Mat) -> Bool

Parameters

`filename`	The filename of a file that contains the dataset information. Each line contains the filename of an image followed by pairs of x and y values of facial landmarks points separated by a space. Example
`images`	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
`facePoints`	The loaded landmark points for all training data.


                    
                    
                    +loadTrainingData:groundTruth:images:facePoints:offset:

A utility to load facial landmark information from the dataset.

Example of usage

cv::String imageFiles = “../data/images_train.txt”; cv::String ptsFiles = “../data/points_train.txt”; std::vector images; std::vectorstd::vector<Point2f > facePoints; loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

example of content in the images_train.txt

/home/user/ibug/image_003_1.jpg /home/user/ibug/image_004_1.jpg /home/user/ibug/image_005_1.jpg /home/user/ibug/image_006.jpg

example of content in the points_train.txt

/home/user/ibug/image_003_1.pts /home/user/ibug/image_004_1.pts /home/user/ibug/image_005_1.pts /home/user/ibug/image_006.pts

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSString *)imageList
             groundTruth:(nonnull NSString *)groundTruth
                  images:(nonnull NSArray<NSString *> *)images
              facePoints:(nonnull Mat *)facePoints
                  offset:(float)offset;

Swift

class func loadTrainingData(imageList: String, groundTruth: String, images: [String], facePoints: Mat, offset: Float) -> Bool

Parameters

`imageList`	A file contains the list of image filenames in the training dataset.
`groundTruth`	A file contains the list of filenames where the landmarks points information are stored. The content in each file should follow the standard format (see face::loadFacePoints).
`images`	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
`facePoints`	The loaded landmark points for all training data.
`offset`	An offset value to adjust the loaded points.


                    
                    
                    +loadTrainingData:groundTruth:images:facePoints:

A utility to load facial landmark information from the dataset.

Example of usage

cv::String imageFiles = “../data/images_train.txt”; cv::String ptsFiles = “../data/points_train.txt”; std::vector images; std::vectorstd::vector<Point2f > facePoints; loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

example of content in the images_train.txt

/home/user/ibug/image_003_1.jpg /home/user/ibug/image_004_1.jpg /home/user/ibug/image_005_1.jpg /home/user/ibug/image_006.jpg

example of content in the points_train.txt

/home/user/ibug/image_003_1.pts /home/user/ibug/image_004_1.pts /home/user/ibug/image_005_1.pts /home/user/ibug/image_006.pts

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSString *)imageList
             groundTruth:(nonnull NSString *)groundTruth
                  images:(nonnull NSArray<NSString *> *)images
              facePoints:(nonnull Mat *)facePoints;

Swift

class func loadTrainingData(imageList: String, groundTruth: String, images: [String], facePoints: Mat) -> Bool

Parameters

`imageList`	A file contains the list of image filenames in the training dataset.
`groundTruth`	A file contains the list of filenames where the landmarks points information are stored. The content in each file should follow the standard format (see face::loadFacePoints).
`images`	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
`facePoints`	The loaded landmark points for all training data.


                    
                    
                    +loadTrainingData:trainlandmarks:trainimages:

This function extracts the data for training from .txt files which contains the corresponding image name and landmarks. The first file in each file should give the path of the image whose landmarks are being described in the file. Then in the subsequent lines there should be coordinates of the landmarks in the image i.e each line should be of the form x,y where x represents the x coordinate of the landmark and y represents the y coordinate of the landmark.

For reference you can see the files as provided in the HELEN dataset

Declaration

Objective-C

+ (BOOL)loadTrainingData:(nonnull NSArray<NSString *> *)filename
          trainlandmarks:(nonnull NSArray<NSArray<Point2f *> *> *)trainlandmarks
             trainimages:(nonnull NSArray<NSString *> *)trainimages;

Swift

class func loadTrainingData(filename: [String], trainlandmarks: [[Point2f]], trainimages: [String]) -> Bool

Parameters

`filename`	A vector of type cv::String containing name of the .txt files.
`trainlandmarks`	A vector of type cv::Point2f that would store shape or landmarks of all images.
`trainimages`	A vector of type cv::String which stores the name of images whose landmarks are tracked

Return Value

A boolean value. It returns true when it reads the data successfully and false otherwise


                    
                    
                    +drawFacemarks:points:color:

Utility to draw the detected facial landmark points

Example of usage

std::vector faces; std::vectorstd::vector<Point2f > landmarks; facemark->getFaces(img, faces); facemark->fit(img, faces, landmarks); for(int j=0;j<rects.size();j++){ face::drawFacemarks(frame, landmarks[j], Scalar(0,0,255)); }

Declaration

Objective-C

+ (void)drawFacemarks:(nonnull Mat *)image
               points:(nonnull Mat *)points
                color:(nonnull Scalar *)color;

Swift

class func drawFacemarks(image: Mat, points: Mat, color: Scalar)

Parameters

`image`	The input image to be processed.
`points`	Contains the data of points which will be drawn.
`color`	The color of points in BGR format represented by cv::Scalar.


                    
                    
                    +drawFacemarks:points:

Utility to draw the detected facial landmark points

Example of usage

std::vector faces; std::vectorstd::vector<Point2f > landmarks; facemark->getFaces(img, faces); facemark->fit(img, faces, landmarks); for(int j=0;j<rects.size();j++){ face::drawFacemarks(frame, landmarks[j], Scalar(0,0,255)); }

Declaration

Objective-C

+ (void)drawFacemarks:(nonnull Mat *)image points:(nonnull Mat *)points;

Swift

class func drawFacemarks(image: Mat, points: Mat)

Parameters

`image`	The input image to be processed.
`points`	Contains the data of points which will be drawn.

Face

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